Data Scientist Deep Learning & NLP

We are seeking a highly skilled Data Scientist with strong expertise in Deep Learning, Natural Language Processing (NLP), and Transfer Learning. The ideal candidate will be hands-on in building, optimizing, and deploying machine learning and neural network models using PyTorch and TensorFlow, with proven experience in working on BERT-based architectures, embeddings, and state-of-the-art NLP frameworks.

Key Responsibilities

• Experience Level: 6 12 Years
• Design, develop, and deploy deep learning models for NLP, computer vision, or multimodal tasks.
• Implement transformer architectures (e.g., BERT, GPT, RoBERTa) for various downstream applications such as text classification, sentiment analysis, summarization, and information retrieval.
• Leverage transfer learning and embeddings to optimize model performance and scalability.
• Research and experiment with cutting-edge deep learning methodologies, bringing innovations into production systems.
• Collaborate with cross-functional teams (engineering, product, data engineering) to integrate models into production environments.
• Perform data preprocessing, feature engineering, and model evaluation using industry best practices.
• Optimize models for efficiency, scalability, and accuracy while addressing challenges such as class imbalance and large-scale data processing.
• Stay updated with the latest advancements in AI, deep learning, and NLP research, applying them where relevant.

• Proven experience (5 years) in developing and deploying ML/DL solutions.
• Strong hands-on expertise with PyTorch and TensorFlow.
• Deep understanding of Neural Networks, Deep Learning, and Transformer-based models (BERT, GPT, etc.).
• Solid knowledge of transfer learning, embeddings, and feature representation techniques.
• Proficiency in Python and related libraries (NumPy, Pandas, Scikit-learn, Hugging Face Transformers).
• Strong experience in model evaluation, hyperparameter tuning, and optimization.
• Experience with cloud platforms (AWS, Google Cloud Platform, Azure) and MLOps tools is a plus.
• Excellent problem-solving skills with the ability to work in fast-paced environments.
• Experience with large language models (LLMs) and fine-tuning.
• Exposure to multimodal learning (vision NLP).
• Knowledge of distributed training and optimization on GPTPUs.
• Familiarity with vector databases (e.g., Pinecone, FAISS, Milvus) for embedding search.
• Master s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related field.